Robust controller design for input‐delayed systems using predictive feedback and an uncertainty estimator

This paper deals with the problem of stabilizing a class of input‐delayed systems with (possibly) nonlinear uncertainties by using explicit delay compensation. It is well known that plain predictive schemes lack robustness with respect to uncertain model parameters. In this work, an uncertainty estimator is derived for input‐delay systems and combined with a modified state predictor, which uses current available information of the estimated uncertainties. Furthermore, based on Lyapunov–Krasovskii functionals, a computable criterion to check robust stability of the closed‐loop is developed and cast into a minimization problem constrained to an LMI. Additionally, for a given input delay, an iterative‐LMI algorithm is proposed to design stabilizing tuning parameters. The main results are illustrated and validated using a numerical example with a second‐order dynamic system. Copyright © 2016 John Wiley & Sons, Ltd.     

An intelligent EGWO-SCA-CS algorithm for PSS parameter tuning under system uncertainties

This paper proposes a novel hybrid technique called enhanced grey wolf optimization-sine cosine algorithm-cuckoo search (EGWO-SCA-CS) algorithm to improve the electrical power system stability. The proposed method comprises of a popular grey wolf optimization (GWO) in an enhanced and hybrid form. It embraces the well-balanced exploration and exploitation using the cuckoo search (CS) algorithm and enhanced search capability through the sine cosine algorithm (SCA) to elude the stuck to the local optima. The proposed technique is validated with the 23 benchmark functions and compared with state-of-the-art methods. The benchmark functions consist of unimodal, multimodal function from which the best suitability of the proposed technique can be identified. The robustness analysis also presented with the proposed method through boxplot, and a detailed statistical analysis is performed for a set of 30 individual runs. From the inferences gathered from the benchmark functions, the proposed technique is applied to the stability problem of a power system, which is heavily stressed with the nonlinear variation of the load and thereby operating conditions. The dynamics of power system components have been considered for the mathematical model of a multimachine system, and multiobjective function has been framed in tuning the optimal controller parameters. The effectiveness of the proposed algorithm has been assessed by considering two case studies, namely, (i) the optimal controller parameter tuning, and (ii) the coordination of oscillation damping devices in the power system stability enhancement. In the first case study, the power system stabilizer (PSS) is considered as a controller, and a self-clearing three-phase fault is considered as the system uncertainty. In contrast, static synchronous compensator (STATCOM) and PSS are considered as controllers to be coordinated, and perturbation in the system states as uncertainty in the second case study.     

Gradient‐based back‐propagation dynamical iterative learning scheme for the neuro‐fuzzy inference system

In this paper, a gradient‐based back propagation dynamical iterative learning algorithm is proposed for structure optimization and parameter tuning of the neuro‐fuzzy system. Premise and consequent parameters of the neuro‐fuzzy model are initialized randomly and then tuned by the proposed iterative algorithm. The learning algorithm is based on the first order partial derivative of the output with respect to the structure parameters. The first order derivative of the model output with respect to the structure parameters determines the sensitivity of the model to structure parameters. The sensitivity values are then used to set the tuning factors and parameters updating step sizes. Therefore, an adaptive dynamical iterative scheme is achieved which adapts the learning procedure to the current state of the performance during the optimization process. Larger tuning step sizes make the convergence speed higher and vice versa. In this regard, this parameter is treated according to the calculated sensitivity of the model to the parameter. The proposed learning algorithm is compared with the least square back propagation method, genetic algorithm and chaotic genetic algorithm in the neuro‐fuzzy model structure optimization. Smaller mean square error and shorter learning time are sought in this paper, and the performance of the proposed learning algorithm is versified regarding these criteria.     

Low-order robust power system stabilizer for single-machine systems: An LMI approach

This paper describes a linear matrix inequality (LMI) approach to the design of robust loworder power system stabilizers (PSSs), which are used to damp out local-mode oscillations of synchronous generators. The performance of a PSS is expressed as the location of the closed-loop poles, and a single fixed-gain pole-placement controller is synthesized for a wide range of operating conditions. The synthesis results in simultaneous regional pole-placement stabilization through static output feedback, and is formulated as a novel LMI feasibility problem with a rank condition. A penalty method is applied to solve the rank-constrained LMI problem. Numerical experiments with a single machine connected to an infinite bus system were performed to demonstrate the proposed LMI method, and the results were compared with those of previous work.     

差分进化法在风电并网系统中的应用

为解决多机风电并网系统的稳定性问题,提出在风电并网系统的同步发电机(SG)中安装电力系统稳定器(PSS),利用差分进化算法解决SG中自动电压调节器(AVR)和PSS参数的最优调节问题。在有、无PSS以及是否使用差分进化法的各种情况下对风电并网系统稳定性进行了研究分析,研究表明通过差分进化法的协同调节使含AVR和PSS的风电并网系统有良好的阻尼作用,能减少发电机转子角差振荡,提高电压稳定性,通过仿真结果对比可知差分进化法可使并网系统稳定性明显提高。     

基于二维系统的迭代学习事件触发鲁棒控制

本文研究了二维系统框架下,带有事件触发机制的不确定离散系统迭代学习鲁棒控制问题.首先为了减少迭代过程中控制信号的更新次数,构建了一种沿迭代轴的事件触发机制,并提出了基于事件触发机制的迭代学习控制算法.基于二维系统理论,将迭代学习过程转化为等价二维Roesser系统.构造李雅普诺夫函数,结合线性矩阵不等式(LMI)技术,给出了系统渐近稳定的充分条件,进一步得到了控制器增益的求取方法.最后仿真结果验证了提出的事件触发机制的有效性.     

An ellipsoid algorithm for probabilistic robust controller design

In this paper, a new iterative approach to probabilistic robust controller design is presented, which is applicable to any robust controller/filter design problem that can be represented as an LMI feasibility problem. Recently, a probabilistic Subgradient Iteration algorithm was proposed for solving LMIs. It transforms the initial feasibility problem to an equivalent convex optimization problem, which is subsequently solved by means of an iterative algorithm. While this algorithm always converges to a feasible solution in a finite number of iterations, it requires that the radius of a non-empty ball contained into the solution set is known a priori. This rather restrictive assumption is released in this paper, while retaining the convergence property. Given an initial ellipsoid that contains the solution set, the approach proposed here iteratively generates a sequence of ellipsoids with decreasing volumes, all containing the solution set. At each iteration a random uncertainty sample is generated with a specified probability density, which parameterizes an LMI. For this LMI the next minimum-volume ellipsoid that contains the solution set is computed. An upper bound on the maximum number of possible correction steps, that can be performed by the algorithm before finding a feasible solution, is derived. A method for finding an initial ellipsoid containing the solution set, which is necessary for initialization of the optimization, is also given. The proposed approach is illustrated on a real-life diesel actuator benchmark model with real parametric uncertainty, for which a robust state-feedback controller is designed.     

Model-free constrained data-driven iterative reference input tuning algorithm with experimental validation

This paper presents the design and experimental validation of a new model-free data-driven iterative reference input tuning (IRIT) algorithm that solves a reference trajectory tracking problem as an optimization problem with control signal saturation constraints and control signal rate constraints. The IRIT algorithm design employs an experiment-based stochastic search algorithm to use the advantages of iterative learning control. The experimental results validate the IRIT algorithm applied to a non-linear aerodynamic position control system. The results prove that the IRIT algorithm offers the significant control system performance improvement by few iterations and experiments conducted on the real-world process and model-free parameter tuning.     

多机电力系统PSS参数优化新方法

机械回路谐振引起了励磁系统频率特性曲线出现“波峰”和“波谷”, 造成现有方法不易准确测量或计算励磁系统相位滞后角. 为此, 本文提出一种新的多机系统PSS参数优化方法. 首先, 重新定义基于多机模型的励磁系统相位滞后角, 并推导出相应的计算公式, 基于新定义的计算方法不再受机械回路谐振的影响, 可以得出更合理的计算结果.然后, 在PSS增益整定时, 将问题描述转化为多机系统部分输出量反馈的优化模型, 通过求解Levine-Athans方程组得到最佳PSS增益. 最后将所提方法应用于一个8机电力系统, 与传统方法比较后, 表明电力系统阻尼特性得到进一步改善.     

Simultaneous linear-quadratic optimal control design via static output feedback

In this paper, the problem of designing a fixed static output feedback control law which minimizes an upper bound on linear quadratic (LQ) performance measures for r distinct MIMO plants is addressed using linear matrix inequality (LMI) technique. An iterative LMI algorithm is proposed to obtain the solution. Examples are used to demonstrate its effectiveness. Copyright ©1999 John Wiley & Sons, Ltd.     

Wind turbine integrated structural and LPV control design for improved closed-loop performance

An iterative redesign algorithm is proposed to integrate the design of the structural parameters and a linear parameter-varying (LPV) controller for a three-bladed horizontal-axis wind turbine. The LPV controller is designed for an eighth-order lumped model of the wind turbine consisting of blades, drive-train and the tower. The lumped model response is matched with detailed open-loop numerical simulations using the Fatigue, Aerodynamics, Structures and Turbulence (FAST) code. The controller is scheduled in real-time based on the mean wind speed to account for the varying system dynamics. The objective is to track the operating trajectory meanwhile minimise the H _∞ performance index from the wind turbulence to the controlled output vector consisting of pitch angle, blade tip deflection, and the generator speed and torque. Sensitivity analysis of the closed-loop performance index with respect to the structural parameters of the system is examined. The integrated design problem is formulated as an iterative sequential controller/structure redesign to obtain the structural parameters and controller matrices corresponding to a local optimal performance index. Each step of the iterative procedure is formulated as a linear matrix inequality (LMI) optimisation problem that can be solved efficiently using available LMI solvers. The evolution of the structural parameters and performance index through the integrated design is illustrated. The FAST closed-loop simulations for two selected designs with the smallest values of the performance index demonstrate the improved performance of the overall system through the integrated structure/control redesign in both minimising the effect of the wind disturbance on the generator output power, and reducing the structural loads on the wind turbine.     

Output feedback MPC with guaranteed robust stability

This paper focuses on the issues of robust stability of model predictive control (MPC). The control problem is formulated as linear matrix inequalities (LMI) optimization problem. A suboptimal solution for the output feedback control problem is proposed. The size of the resulting MP controller is reduced by using a suitable state-space representation of the process. Guaranteed stability conditions for the output feedback MPC are enforced via a Lyapunov type constraint. An iterative algorithm is developed resulting in a pair of coupled LMI optimization problems which provide a robustly stable output feedback gain. Model uncertainties are considered via a polytopic set of process models. The methodology is illustrated with the simulation of the control problem of two chemical processes. The results show that the proposed strategy eliminates the need to detune the MP controller improving the performance for most of the cases considered.     

不确定时滞网络控制系统的状态反馈控制

在网络传输过程中, 系统的稳定性受不确定, 网络延迟, 丢包和错序所影响. 本文针对这些非理想网络状况,研究了网络控制系统状态反馈控制. 建立连续时间系统模型时, 还引入了一个定常状态时延, 使得模型更贴近实际.随后, 运用改进的Lyapunov-Krasovskii函数, 推导出网络控制系统鲁棒稳定的充分条件, 并基于该充分条件得到利用线性矩阵不等式(LMI)的控制器设计方法, 再运用迭代算法求解相关系数. 最后通过MATLAB数值仿真算例, 证明了本文方法的正确性和有效性.     

基于改进BBO算法的水轮发电机组PSS2B参数优化

为了使电力系统稳定器(PSS)参数能够适应电力系统的多种运行方式,本文提出了一种可用于PSS参数优化的算法。该算法结合差分进化算法(DE)变异操作和自适应迁移操作对生物地理学算法(BBO)进行改进,提高了寻优能力和速度。最后,借助电力系统数字仿真软件对该算法在PSS参数优化方面的有效性进行验证,仿真结果表明:经本文所提优化算法计算的PSS参数能够适应电力系统的不同运行工况。     

在线VRFT数据驱动控制方法及其仿真研究*

虚拟参考反馈整定(VRFT)是一种离线的数据驱动控制器参数整定的方法,要求整定过程中对象特性保持不变。本文针对离线算法的不足,提出了一种在线VRFT数据驱动算法。首先利用滤波器改变了离线算法的时序,得到用于实时运算的有效数据。然后提出了基于带遗忘因子递推最小二乘法的VRFT控制器参数辨识方法,不依赖于对象模型,完全利用实时数据实现了在线控制器参数整定。仿真结果表明, 在对象特性变化较大的情况下,在线VRFT整定方法优于传统的离线VRFT方法,具有很好的自适应性。     

Robust tuning for machine-directional predictive control of MIMO paper-making processes

This paper solves the controller tuning problem of machine-directional predictive control for multiple-input–multiple-output (MIMO) paper-making processes represented as superposition of first-order-plus-dead-time (FOPDT) components with uncertain model parameters. A user-friendly multi-variable tuning problem is formulated based on user-specified time domain specifications and then simplified based on the structure of the closed-loop system. Based on the simplified tuning problem and a proposed performance evaluation technique, a fast multi-variable tuning technique is developed by ignoring the constraints of the MPC. In addition, a technique to predict the computation time of the tuning algorithm is proposed. The efficiency of the proposed method is verified through Honeywell real time simulator platform with a MIMO paper-making process obtained from real data from an industrial site.     

基于LMI的μ方法及其在电力系统中的应用

提出了一个新的基于linear matrix inequality(LMI)的混合结构奇异值(μ)分析与综合方法,通过采用S-过程,将Robust LMI形式的判据简化,得到了可验证的基于LMI的μ分析综合判据.该判据是基于状态空间描述的,从而消除了频率扫描过程和频率响应曲线拟合过程并具有更好的数值性态.将该方法应用于典型电力系统励磁控制的鲁棒稳定性分析和鲁棒控制器的设计中,数值仿真结果同现有AVR+PSS控制器的结果所做比较表明该方法的有效性.     

结合前馈调参与迭代学习的数据驱动控制方法

在前馈控制中,需要尽可能的去除前馈控制器对系统模型的需求,同时保证高精度和鲁棒性.本文提出了一种数据驱动的将迭代前馈调参与迭代学习控制进行结合的方法,通过引入基函数参数化的前馈控制器和输入整形滤波器,使用梯度下降法求解最优系统前馈控制器,消除期望轨迹引入的扰动;通过迭代学习控制,消除系统重复性扰动,进一步提高控制精度.算法具有不依赖系统模型,高精度,适用于变轨迹任务的优点.文中给出了相应的仿真,并应用到一个直线电机系统,通过实验验证了算法的有效性.     

Determination of optimal hierarchical fuzzy controller parameters according to loading condition with ANN

This paper represents an artificial neural network (ANN) backpropagation algorithm is used to choose best coefficients of hierarchical fuzzy power system stabilizer (HFPSS). PSS is used for stability enhancement of a single machine infinite bus (SMIB) power system. ANN algorithm is used to predict load condition of the power system. And according to the predicted load condition ANN determinates choosing optimal parameters of the hierarchical fuzzy controller (HFC) to achieve better performance. Simulation results are compared with conventional PSS (CPSS) to show the effectiveness of the proposed controller. Also quantitative criterias of measuring performance is computed for 16 loading conditions.     

Eigenvalue assignments in multimachine power systems using tabu search algorithm

Applying tabu search (TS) optimization technique to multimachine power system stabilizer (PSS) design is presented in this paper. The proposed approach employs TS to search for optimal or near optimal settings of PSS parameters that shift the system eigenvalues associated with the electromechanical modes to the left of a vertical line in the s-plane. Incorporation of TS algorithm in PSS design significantly reduces the computational burden. One of the main advantages of the proposed approach is its robustness to the initial guess. The performance of the proposed PSS under different disturbances and loading conditions is investigated for multimachine power systems. The eigenvalue analysis and the nonlinear simulation results show the effectiveness of the proposed PSSs to damp out the local as well as the interarea modes of oscillations and work effectively over a wide range of loading conditions and system configurations.